
RLCache: Automated Cache Management Using Reinforcement Learning
This study investigates the use of reinforcement learning to guide a gen...
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Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks
We study the problem of causal discovery through targeted interventions....
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Statistical Agnostic Mapping: a Framework in Neuroimaging based on Concentration Inequalities
In the 70s a novel branch of statistics emerged focusing its effort in s...
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ncRNA Classification with Graph Convolutional Networks
Noncoding RNA (ncRNA) are RNA sequences which don't code for a gene but...
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ResourceEfficient Neural Networks for Embedded Systems
While machine learning is traditionally a resource intensive task, embed...
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Advance Prediction of Ventricular Tachyarrhythmias using Patient Metadata and MultiTask Networks
We describe a novel neural network architecture for the prediction of ve...
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Continual Learning with Adaptive Weights (CLAW)
Approaches to continual learning aim to successfully learn a set of rela...
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The AnimalAI Environment: Training and Testing AnimalLike Artificial Cognition
Recent advances in artificial intelligence have been strongly driven by ...
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iUNets: Fully invertible UNets with Learnable Up and Downsampling
UNets have been established as a standard neural network design archite...
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MEME: Generating RNN Model Explanations via Model Extraction
Recurrent Neural Networks (RNNs) have achieved remarkable performance on...
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Should Artificial Intelligence Governance be Centralised? Design Lessons from History
Can effective international governance for artificial intelligence remai...
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Transformation Consistency Regularization A SemiSupervised Paradigm for ImagetoImage Translation
Scarcity of labeled data has motivated the development of semisupervise...
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Extracting more from boosted decision trees: A high energy physics case study
Particle identification is one of the core tasks in the data analysis pi...
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Deep active inference agents using MonteCarlo methods
Active inference is a Bayesian framework for understanding biological in...
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A Deterministic Approach to Avoid Saddle Points
Loss functions with a large number of saddle points are one of the main ...
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On instabilities of deep learning in image reconstruction  Does AI come at a cost?
Deep learning, due to its unprecedented success in tasks such as image c...
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Learning a SpatioTemporal Embedding for Video Instance Segmentation
We present a novel embedding approach for video instance segmentation. O...
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Stochastic Optimization for Regularized Wasserstein Estimators
Optimal transport is a foundational problem in optimization, that allows...
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Variational Crossdomain Natural Language Generation for Spoken Dialogue Systems
Crossdomain natural language generation (NLG) is still a difficult task...
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FastSCNN: Fast Semantic Segmentation Network
The encoderdecoder framework is stateoftheart for offline semantic i...
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Improving and Understanding Variational Continual Learning
In the continual learning setting, tasks are encountered sequentially. T...
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Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care
Clinical decision making is challenging because of pathological complexi...
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MixedVariable Bayesian Optimization
The optimization of expensive to evaluate, blackbox, mixedvariable fun...
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Universal Masking is Urgent in the COVID19 Pandemic: SEIR and Agent Based Models, Empirical Validation, Policy Recommendations
We present two models for the COVID19 pandemic predicting the impact of...
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On Learnability under General Stochastic Processes
Statistical learning theory under independent and identically distribute...
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SpatioTemporal Analysis of Facial Actions using LifecycleAware Capsule Networks
Most stateoftheart approaches for Facial Action Unit (AU) detection r...
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Dynamic Spectral Residual Superpixels
We consider the problem of segmenting an image into superpixels in the c...
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Adaptive Prediction Timing for Electronic Health Records
In realistic scenarios, multivariate timeseries evolve over casebycase...
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TwoStage Sparse Regression Screening to Detect BiomarkerTreatment Interactions in Randomized Clinical Trials
Highdimensional biomarkers such as genomics are increasingly being meas...
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Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks
Influence maximization is a widely studied topic in network science, whe...
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Deconfounding Reinforcement Learning in Observational Settings
We propose a general formulation for addressing reinforcement learning (...
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Improved Algorithm on Online Clustering of Bandits
We generalize the setting of online clustering of bandits by allowing no...
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Mimicry: Towards the Reproducibility of GAN Research
Advancing the state of Generative Adversarial Networks (GANs) research r...
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Masked Language Modeling for Proteins via Linearly Scalable LongContext Transformers
Transformer models have achieved stateoftheart results across a diver...
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AutoNCP: Automated pipelines for accurate confidence intervals
Successful application of machine learning models to realworld predicti...
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Path Integral Based Convolution and Pooling for Graph Neural Networks
Graph neural networks (GNNs) extends the functionality of traditional ne...
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Scalable Bayesian Inverse Reinforcement Learning
Bayesian inference over the reward presents an ideal solution to the ill...
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NonAutoregressive Text Generation with Pretrained Language Models
Nonautoregressive generation (NAG) has recently attracted great attenti...
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A General Framework for Implicit and Explicit Debiasing of Distributional Word Vector Spaces
Distributional word vectors have recently been shown to encode many of t...
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Variational Orthogonal Features
Sparse stochastic variational inference allows Gaussian process models t...
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βCores: Robust LargeScale Bayesian Data Summarization in the Presence of Outliers
Modern machine learning applications should be able to address the intri...
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Achieving Adversarial Robustness via Sparsity
Network pruning has been known to produce compact models without much ac...
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Scoring Lexical Entailment with a Supervised Directional Similarity Network
We present the Supervised Directional Similarity Network (SDSN), a novel...
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Creatures great and SMAL: Recovering the shape and motion of animals from video
We present a system to recover the 3D shape and motion of a wide variety...
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Superpixel Contracted GraphBased Learning for Hyperspectral Image Classification
A central problem in hyperspectral image classification is obtaining hig...
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Federated PCA with Adaptive Rank Estimation
In many online machine learning and data science tasks such as data summ...
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GraphX^NET Chest XRay Classification Under Extreme Minimal Supervision
The task of classifying Xray data is a problem of both theoretical and ...
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SemEval2013 Task 4: Free Paraphrases of Noun Compounds
In this paper, we describe SemEval2013 Task 4: the definition, the data...
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There is Strength in Numbers: Avoiding the HypothesisOnly Bias in Natural Language Inference via Ensemble Adversarial Training
Natural Language Inference (NLI) datasets contain annotation artefacts r...
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Principal Neighbourhood Aggregation for Graph Nets
Graph Neural Networks (GNNs) have been shown to be effective models for ...
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University of Cambridge
The University of Cambridge is a collegiate public research university in Cambridge, United Kingdom.